IVUS image analysis method

ABSTRACT

Disclosed in an intravascular ultrasound (IVUS) image analysis method, comprising the steps of: allowing a computer to acquire an IVUS image of an object; segmenting a constituent element included in the IVUS image; and determining the constituent parts and the degree of risk of plaque included in the IVUS image.

TECHNICAL FIELD

The present invention relates to an intravascular ultrasound (IVUS)image analysis method.

BACKGROUND ART

Intravascular ultrasound (IVUS), also known as endovascular ultrasound,is used to insert a catheter having an ultrasound device into bloodvessels and acquire ultrasound images of the interior of the bloodvessels.

Acquired IVUS images are reviewed by doctors and are mainly used toquantitatively determine patients' coronary artery diseases or stenosislevels.

In the case of an experienced expert, some necessary information can beacquired using only IVUS images, but virtual histology (VH)-IVUS ornear-infrared spectroscopy (NIRS) is also used to assist with theacquisition.

Recently, development has been actively conducted on a method ofprocessing various medical images using deep learning to acquirenecessary information.

Deep learning is defined as a set of machine-learning algorithms thatattempt high-level abstraction (which is a task of abstracting keycontent or functions in a large amount of data or complex data) througha combination of several nonlinear transformation methods. In the bigpicture, deep learning can be seen as a field of machine learning thatteaches human ways of thinking to computers.

DISCLOSURE Technical Problem

The present invention is directed to providing an intravascularultrasound (IVUS) image analysis method.

Technical problems to be solved by the present invention are not limitedto the aforementioned problem, and other problems that are not mentionedhere can be clearly understood by those skilled in the art from thefollowing description.

Technical Solution

IVUS image analysis method according to an aspect of the presentinvention for solving the above mentioned problems, may compriseobtaining, by computer, an IVUS (IntraVascular UltraSound) image of atarget, performing segmentation of components included in the IVUSimage, determining constituents and a degree of risk of plaque includedin the IVUS image.

Also, the step performing the segmentation may comprise performing thesegmentation of the components included in the IVUS image using a modeltrained using labeled IVUS image.

Also, The IVUS image analysis method, may further comprise, determininglocation of the plaque included in the IVUS image.

Also, the IVUS image analysis method, may further comprise, presentingblood vessel in predetermined range of the target and presenting thedetermined location of the plaque in the presented blood vessel.

Also, the IVUS image analysis method, may further comprise, presenting arisk zone, based on the location of the plaque and the constituents andthe degree of risk of the plaque.

Also, the step determining the constituents of the plaque may furthercomprise, determining the constituents of the plaque using a modeltrained using labeled IVUS image and processed IVUS image.

Also, the processed IVUS image may include, at least one of an image inwhich elements included in the IVUS image are marked differentlyaccording to the constituents or an image which is made by combining theNIRS (Near InfraRed Spectroscopy) result to the IVUS image.

Also, the step determining the degree of risk of the plaque may furthercomprise, determining the degree of the risk of the plaque based on theconstituents of the plaque.

Also, the step determining the degree of risk of the plaque may furthercomprise, determining the degree of the risk of the plaque using a modeltrained using IVUS image labeled with a presence or absence of acardio-crerbrovascular disease.

According to an aspect of the present invention for solving theabove-described problem, a computer program stored in a computerreadable media to perform the method of claim 1 in combination with acomputer that is hardware is provided.

Other specifics of the invention are included in the detaileddescription and drawings.

Advantageous Effects

According to the disclosed embodiment, it is possible to automaticallydetermine the location, constituents, and degree of risk of plaqueswhich are difficult to visually determine using the IVUS image.

Advantageous effects of the present invention are not limited to theaforementioned effects, and other advantageous effects that are notdescribed herein will be clearly understood by those skilled in the artfrom the following description.

DESCRIPTION OF DRAWINGS

FIG. 1 is a flowchart showing an intravascular ultrasound (IVUS) imageanalysis method according to an embodiment.

FIG. 2 is a diagram showing an example of a method of acquiring an IVUSimage.

FIG. 3 is a diagram showing an example of a computer segmenting an IVUSimage.

FIG. 4 is a diagram showing an example of a method of providingadditional information for an IVUS image using near-infraredspectroscopy (NIRS).

FIG. 5 is a diagram showing an example of acquiring a TVC image.

FIG. 6 is a diagram showing an example of a method of training a modelfor acquiring a TVC image from an IVUS image of a coronary artery.

FIG. 7 is a diagram showing an example of a virtual histology (VH)-IVUSimage.

FIG. 8 is a diagram showing a method of analyzing an IVUS imageaccording to an embodiment.

FIG. 9 is a diagram showing a result of analyzing an IVUS imageaccording to an embodiment.

MODE FOR CARRYING OUT THE INVENTION

Advantages and features of the present invention and implementationmethods thereof will be clarified through the following embodimentsdescribed in detail with reference to the accompanying drawings.However, the present invention is not limited to embodiments disclosedherein and may be implemented in various different forms. Theembodiments are provided for making the disclosure of the presentinvention thorough and for fully conveying the scope of the presentinvention to those skilled in the art. It is to be noted that the scopeof the present invention is defined by the claims.

The terminology used herein is for the purpose of describing particularembodiments by way of example only and is not intended to be limiting tothe disclosed invention. As used herein, the singular forms “a,” “an,”and “one” include the plural unless the context clearly indicatesotherwise. The terms “comprises” and/or “comprising” used herein specifythe presence of stated elements but do not preclude the presence oraddition of one or more other elements. Like reference numerals refer tolike elements throughout the specification, and the term “and/or”includes any and all combinations of one or more of the associatedlisted items. It will be also understood that, although the terms first,second, etc. may be used herein to describe various elements, theseelements should not be limited by these terms. These terms are only usedto distinguish one element from another. Thus, a first element could betermed a second element without departing from the technical spirit ofthe present invention.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this invention belongs. It will befurther understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andwill not be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

The term “unit” or “module” used herein refers to a software element ora hardware element such as a field-programmable gate array (FPGA) or anapplication-specific integrated circuit (ASIC), and a “unit” or “module”performs any role. However, a “unit” or “module” is not limited tosoftware or hardware. A “unit” or “module” may be configured to be in anaddressable storage medium or to execute one or more processors.Accordingly, as an example, a “unit” or “module” includes elements suchas software elements, object-oriented software elements, class elements,and task elements, processes, functions, attributes, procedures,sub-routines, segments of program codes, drivers, firmware, micro-codes,circuits, data, database, data structures, tables, arrays, andvariables. Furthermore, functions provided in elements and “units” or“modules” may be combined as a smaller number of elements and “units” or“modules” or further divided into additional elements and “units” or“modules.”

Spatially relative terms, such as “below,” “beneath,” “lower,” “above,”“upper,” “on,” “between,” and the like, may be used herein for ease ofdescription to describe the relationship of one element to anotherelement(s) as illustrated in the figures. It will be understood that thespatially relative terms are intended to encompass differentorientations of elements in use or operation in addition to theorientation depicted in the figures. For example, if an element shown inthe drawing is turned over, an element described as “below” or “beneath”another component could be placed “above” the element. Thus, theexemplary term “below” or “beneath” may encompass both orientations of“above” and “below” or “beneath.” An element may be otherwise orientedand the spatially relative descriptors used herein interpretedaccordingly.

The term “image” used herein may refer to multi-dimensional datacomposed of discrete image elements (e.g., pixels in a two-dimensional(2D) image and voxels in a three-dimensional (3D) image).

The term “object” used herein may refer to a person or animal or theentirety or a part of a person or an animal. For example, an object mayinclude at least one of blood vessels and organs such as liver, heart,uterus, brain, breast, and abdomen. Also, an “object” may be a phantom.A phantom refers to a material having a volume very close to theeffective atomic number and density of an organism and may include asphere phantom having properties similar to a human body.

The term “user” used herein may refer to a doctor, a nurse, a clinicalpathologist, or a medical imaging expert as a medical expert or mayrefer to a technician who repairs a medical device, but the presentinvention is not limited thereto.

Hereinafter, embodiments of the present invention will be described indetail with reference to the accompanying drawings.

FIG. 1 is a flowchart showing an intravascular ultrasound (IVUS) imageanalysis method according to an embodiment.

In the disclosed embodiment, each step shown in FIG. 1 is performed by acomputer, and the term “computer” used herein encompasses a computingdevice including at least one processor.

In step S110, a computer acquires an IVUS image of an object.

In the disclosed embodiment, the computer may be directly connected toan IVUS imaging apparatus in a laboratory or may acquire an IVUS imageremotely through a network or server

Referring to FIG. 2, an example of a method of acquiring an IVUS imageis shown.

In detail, referring to FIG. 2, an example of acquiring an IVUS image300 by inserting a catheter 200 into a blood vessel 10 is shown.

An ultrasonic transducer 210 rotating at high speed is provided at theend of the catheter 200 and is used to acquire an ultrasound image ofthe interior of the blood vessel 10. The acquired ultrasound image isused to derive opinions about the inner diameter, area, shape, bloodvessel wall thickness, atheroma, plaque components, and blood clots ofthe blood vessel 10.

In an embodiment, a plaque 20 may be in the blood vessel 10.

The plaque 20 is cholesterol selectively absorbed and accumulated on theinner wall (lumen) of the blood vessel 10, and intravascular celldebris, smooth muscle cells, and macrophages are accumulated while aplaque is being formed. The accumulated plaque calcifies and protrudesinto the blood vessel and thus may interfere with blood flow.

Referring to the IVUS image 300, the shape of the blood vessel and theaccumulation of the plaque can be seen.

For example, an outer wall 302 (vessel), an inner wall 304, and a plaque306 can be seen in the IVUS image 300. The IVUS image 300 may beanalyzed and used by skilled medical personnel.

In an embodiment, the IVUS image 300 may be stored with the labeling ofthe locations of the inner and outer walls, and furthermore, thelocation of the plaque for the purpose of learning.

In step S120, the computer segments the elements included in the IVUSimage 300 acquired in step S110.

In an embodiment, the computer segments the locations of the inner walland the outer wall of the blood vessel included in the IVUS image 300.

Referring to FIG. 3, an example of the computer segmenting the IVUSimage 300 is shown.

In an embodiment, the computer segments the elements included in theIVUS image 300 using a model 400 which is trained using labeled IVUSimages.

For example, the labeled IVUS images may be images in which thelocations of the inner wall and the outer wall of the blood vessel aremarked, and the trained model 400 may be a model that is trained byperforming deep learning using the labeled IVUS images.

In an embodiment, the trained model 400 may be a model that uses U-NET.

U-NET is a kind of convolutional neural network that performs quick andaccurate segmentation and is primarily used to segment medical images.

The model 400 outputs a result image obtained by segmenting the IVUSimage 300. There is no limitation on the shape of the output image, andby using the resulting image, the computer may acquire an image obtainedby adding the outer wall 310 and the inner wall 312 to the original IVUSimage 300 as shown in FIG. 3.

In step S130, the computer determines the degree of risk andconstituents of a plaque included in the IVUS image acquired in stepS110.

As shown in FIG. 2, the outer wall 302 and the inner wall 304 of theblood vessel are represented in the IVUS image 300, and thus it ispossible to visually check the degree of thickening of the blood vesselwalls. Skilled experts can also estimate the location and composition ofplaques contained in the thickened blood vessel even with the naked eye,but since this is not an accurate analysis, a means for assisting withdetermination is used.

Referring to FIG. 4, an example of a method of providing additionalinformation for an IVUS image using near-infrared spectroscopy (NIRS) isshown.

NIRS is an examination method that can determine the composition of anobject through a spectrum obtained by emitting near-infrared rays. Inthe disclosed embodiment, the method is used to examine a blood vesselwall to mark the location of a lipid component.

Among the plaques, lipid-containing plaques cause arteriosclerosis andother cardiovascular diseases, so it is important to know the locationsand amount of the lipid-containing plaques to determine a patient'svascular health.

In an embodiment, as shown in FIG. 4, an NIRS image 510 which is aresult obtained by using NIRS and in which a part containing lipid isrepresented in yellow may be provided together with an IVUS image 500,and thus it is possible for a user to accurately understand thecomposition of the plaque through the NIRS image 510.

As an example, the image shown in FIG. 4 may be acquired using TVC.

A TVC image is acquired by a TVC image system of Infraredx Inc. andprovides information on the size and chemical composition of a lesionthrough near-infrared spectroscopy and ultrasound images of the interiorof blood vessels.

Hereinafter, a TVC image is used as a term referring to an ultrasoundimage of the interior of a blood vessel and an image acquired throughnear-infrared spectroscopy and is not necessarily limited to an imageobtained by a TVC image system of Infraredx Inc. In this specification,the term “IVUS-NIRS image” is also used in the same sense as the term“TVC image.”

FIG. 5 is a diagram showing an example of acquiring a TVC image.

Referring to FIG. 5, the heart 30 of an object is shown.

A TVC image is acquired using near-infrared spectroscopy and ultrasoundimages of the interior of a coronary artery. By using a TVC image, it ispossible to acquire sectional views of a coronary artery 10 fromdifferent viewpoints as shown in FIG. 5.

Referring to a cross-sectional view 40 shown in FIG. 5, the locations ofa plaque 20 and a blood vessel wall 12 of the coronary artery 10 areshown. The locations of the blood vessel wall 12 and the plaque 20 maybe displayed in specific shapes or as visualized images, as shown inFIG. 5, or may be colored as shown in FIG. 4. Accordingly, it ispossible for the user to easily determine a location where a plaque isformed in the coronary artery 10 or where stenosis progresses.

Also, by using TVC, a longitudinal sectional view 50 having a differentform may be acquired. Referring to the longitudinal sectional view,along with an IVUS image corresponding to one section in the coronaryartery 10, information about the composition of the plaque is displayedon the edge of the IVUS image. The longitudinal sectional view 50corresponds to the NIRS image 510 shown in FIG. 4.

In an embodiment, the computer may estimate and generate a TVC imageincluding an NIRS analysis result from the IVUS image.

FIG. 6 is a diagram showing an example of a method of training a modelfor acquiring a TVC image from an IVUS image of a coronary artery.

A TVC image 500 of a coronary artery of an object is shown in FIG. 6.The TVC image 500 may be divided into a part 502 indicating the interiorof a blood vessel, which can be acquired using IVUS, and a part 510indicating the characteristics of a plaque inside the blood vessel,which can be acquired using NIRS.

In an embodiment, the computer may cut the TVC image 500 at apredetermined angle. For example, as shown in FIG. 6, the computer maysplit the TVC image 500 into rectangles 512 and 514 which are inclined10 degrees from one another.

The computer may determine the composition of a plaque contained in eachrectangle using the part 510 indicating the characteristics of theplaque inside the blood vessel which is included in the correspondingrectangle.

For example, as shown in FIG. 6, the rectangles 512 and 514 may beprocessed into pieces of learning data 520 and 530 labeled on the basisof the composition of each plaque.

For example, the pieces of learning data 520 and 530 may be labeled on acolor basis. For example, in the case of using NIRS, lipid-containingplaques are represented in yellow, and the other plaques are representedin red or brown. Accordingly, it is possible to label learning databased on color and perform learning.

In an embodiment, the computer may recognize the colors of therectangles 512 and 514 to automatically generate labeled pieces oflearning data 520 and 530.

The computer trains the model with the labeled pieces of learning data520 and 530.

When a new IVUS image is acquired, the computer cuts the acquired IVUSimage at a predetermined angle, inputs image cuts to the trained model,and acquires the composition of a plaque included in each image cut.There is no limitation to the output method of the trained model, andthe composition of the plaque included in the image cut may be outputusing a numerical value, color, or the like.

The computer may estimate and acquire a TVC image from the IVUS image onthe basis of a result of determining the image cuts.

In an embodiment, the computer determines the location of a plaqueincluded in an IVUS image. The computer may determine the location of aplaque included in a single IVUS image. For example, the computer maysegment a single IVUS image and then determine and display the locationof a plaque.

Also, the computer may determine and display the location of a plaque ina blood vessel within a predetermined range of an object usingconsecutive IVUS images. For example, the computer may schematicallydisplay a blood vessel within a predetermined range 10 as shown in FIG.5 but may determine and display the location of a plaque as shown in thecross-sectional view 40.

In the disclosed embodiment, virtual histology (VH)-IVUS may be used asa secondary means for the user or computer to determine the location andcomposition of the plaque.

Referring to FIG. 7, an example of a VH-IVUS image is shown.

In the disclosed embodiment, a VH-IVUS image collectively refers to animage in which each element included in an IVUS image is displayeddifferently depending on the composition and is not limited to an imageacquired using a specific company or specific equipment.

For example, a VH-IVUS image may refer to a result obtained by coloringthe composition of the plaque included in the IVUS image.

In the disclosed embodiment, a VH-IVUS image may be utilized as learningdata for determining the location and composition of the plaque.

FIG. 8 is a diagram showing a method of analyzing an IVUS imageaccording to an embodiment.

Referring to FIG. 8, an example of training a model 700 using learningdata 710 is shown.

In an embodiment, at least one of an original IVUS image, a labeled IVUSimage, a VH-IVUS image, and an IVUS-NIRS image may be used as thelearning data 710. In addition, various data may be utilized as thelearning data.

The model 700 may be trained through deep learning using the learningdata 710, and there is no limitation on the specific training method.

In an embodiment, the IVUS-NIRS image is learned using the methoddescribed with reference to FIGS. 5 and 6, the VH-IVUS may be learned inthe same way or a similar way to the labeled IVUS image, and the VH-IVUSimage may be utilized as secondary data used to label the IVUS image.

The computer may analyze the IVUS image 300 using the trained model 700and acquire the analysis result 800.

The analysis result 800 may be acquired in the form of an IVUS image inwhich the location and composition of the plaque are represented, forexample, an image similar to a VH-IVUS image or an IVUS-NIRS image, andmay also be acquired as separate analysis data. The analysis data may beacquired in the form of an image and may also be acquired in the form oftext containing numbers.

In an embodiment, pre-processing and data cleaning of the learning data710 may be performed first.

For example, a labeling task may be performed as the preprocessing ofthe learning data 710, and incorrect or inappropriate images may beexcluded from the learning data.

In an embodiment, when unclassified learning data is acquired, thecomputer may classify whether each image included in the learning datais an IVUS image, a labeled IVUS image, a VH-IVUS image, or an IVUS NIRSimage.

A classifier for classifying the learning data may be generated throughtraining, and the computer may classify each image on the basis of thecharacteristics of the corresponding image. In this case, whether thelearning data is correctly classified in the pre-processing process forthe learning data may be determined.

In an embodiment, the feedback of the analysis result 800 may beperformed. For example, feedback on whether the analysis result 800 isreviewed by the user and analyzed correctly or whether the analysisresult is valid may be received.

In another embodiment, the feedback process may be automaticallyperformed by analyzing the IVUS image included in the learning data 710using the trained model 700 as part of the learning process and then bycomparing the analysis result 800 to the learning data 710.

The computer may perform reinforcement learning on the model 700 on thebasis of the above-described feedback result.

In an embodiment, the computer may determine the degree of risk of aplaque included in a blood vessel of an object on the basis of the IVUSimage 300.

In an embodiment, the computer may determine the constituents of theplaque included in the blood vessel of the object using theabove-described method and may determine the degree of risk of theplaque on the basis of the determined constituent.

For example, when the plaque contains lipid, the computer may determinethat the plaque is in danger.

In an embodiment, the computer may label each IVUS image withinformation on a health status of an object of which the correspondingIVUS image is captured. For example, the computer may label an IVUSimage with a health status of an object of which the IVUS image iscaptured, the health status including information about a cardiovasculardisease of the object.

The computer may acquire the trained model using the IVUS image labeledwith the health status of the object.

The computer may infer and acquire whether an object has acardiovascular disease by inputting the IVUS image to the trained model.Likewise, by inputting the IVUS image to the trained model, the computermay determine whether a plaque included in the IVUS image causes theobject's cardiovascular disease. That is, the computer may input theIVUS image to the trained model to determine the degree of risk of theplaque included in the IVUS image.

FIG. 9 is a diagram showing a result of analyzing an IVUS imageaccording to an embodiment.

The computer may acquire one or more IVUS images 300 of at least aportion 902 of the object's cardiovascular artery 900 and determine thelocation, composition, and degree of risk of the plaque included in theobject's cardiovascular artery 902 on the basis of the acquired IVUSimages.

In an embodiment, the computer may schematically display the locationand composition of the plaque included in the object's cardiovascularartery 902 on the basis of the determination result. For example, likean image 910 shown in FIG. 9, the cardiovascular artery 902 may beschematized with different colors depending on the section, and asection in danger, which includes a plaque in danger (e.g., alipid-containing plaque), may be marked (e.g., in yellow).

Also, like another image 920 shown in FIG. 9, the location and size ofthe plaque with risk may be displayed while the cardiovascular artery902 is schematized with colors. Likewise, the computer may alsoschematize an image in three dimensions or in the form of a top view ofa three-dimensional (3D) image, and thus can display the accuratelocation and size of the plaque with risk.

Also, the computer may directly display the location, size, sectionswith risk, and the like of the plaque in a cardiovascular image 900(e.g., a coronary angiography image) of the object instead of aschematized image.

In an embodiment, the analysis method according to the disclosedembodiment may be used in real-time not only for examination but alsofor treatments and surgeries.

For example, when a cardiovascular stent treatment or a plaque removaltreatment is performed on an object, a catheter for acquiring an IVUSimage may also be equipped with equipment for the stent or plaqueremoval treatment.

According to the disclosed embodiment, since a computer automaticallydetermines the location, composition, and degree of danger of a plaqueusing only an IVUS image, it is possible to check a location requiring astent treatment or a plaque removal treatment in real-time.

A user can determine the type of a treatment required at a location intowhich a catheter is inserted on the basis of information acquired inreal-time and can immediately proceed with the treatment.

Therefore, by utilizing the disclosed embodiment, examination andtreatment may be performed with only one invasive technique withoutneeding to perform an invasive examination and an invasive treatmentseparately.

The steps of a method or algorithm described in connection with anembodiment of the present invention may be embodied directly inhardware, in a software module executed by hardware, or a combination ofthe two. A software mode may reside in random-access memory (RAM),read-only memory (ROM), erasable programmable ROM (EPROM), electricallyerasable programmable ROM (EEPROM), flash memory, a hard disk, aremovable disk, a CD-ROM, or any form of computer-readable recordingmedium that is known in the art.

Although embodiments of the present invention have been described withreference to the accompanying drawings, those skilled in the art willappreciate that various modifications and alterations may be madetherein without departing from the technical spirit or essential featureof the present invention. Therefore, it should be understood that theabove embodiments are illustrative rather than restrictive in allrespects.

The invention claimed is:
 1. A method of performing analysis of acardiovascular image of a subject, comprising: obtaining a first imageof at least a partial region of a cardiovascular of the subject; andobtaining a second image by processing the first image based on adiagnosis assistance model, the diagnosis assistance model being trainedbased on a first blood vessel image and a second blood vessel imagecorresponding to the first blood vessel image as a labeled data; whereinthe first image and the first blood vessel image are taken with a sameimaging method, wherein the second image is generated based on the firstimage to include analysis information on at least a partial region ofthe first image, wherein an object of the second blood vessel imagecorresponds to an object of the first blood vessel image, wherein alocation of the cardiovascular vessel captured in the second bloodvessel image corresponds to a location of the cardiovascular vesselcaptured in the first blood vessel image, and wherein the first bloodvessel image and the second blood vessel image are taken with differentimaging methods.
 2. The method of claim 1, wherein the analysisinformation includes numerical information or color information based ona type of component of a plaque included in at least a partial region ofthe first image.
 3. The method of claim 1, wherein the analysisinformation includes color information displayed so that a location of alipid component and a blood vessel outer wall region included in thefirst image correspond when the analysis information includesinformation on whether a plaque contains a lipid component and alocation of the lipid component.
 4. The method of claim 1, wherein theanalysis information is displayed in a first color indicating that acomponent of a plaque contains a lipid component or a second colorindicating that the component of the plaque does not contain the lipidcomponent, and wherein the first color and the second color aredifferent.
 5. The method of claim 1, wherein the diagnosis assistancemodel labels whether a plaque included in the second blood vessel imagecontains a lipid component, wherein the diagnosis assistance model islabeled with a third color when the plaque included in the second bloodvessel image contains a lipid component, and wherein the diagnosisassistance model is labeled with a fourth color when the plaque includedin the second blood vessel image does not contain a lipid component, andthe third color and the fourth color are different.
 6. The method ofclaim 1, wherein the diagnosis assistance model labels whether a plaqueincluded in the second blood vessel image contains a lipid component anda location of the plaque.
 7. The method of claim 1, wherein the firstblood vessel image is divided into a plurality of first rectangularregions rotated at a predetermined angle of 10 degrees or less, whereinthe second blood vessel image is divided into a plurality of secondrectangular regions rotated at the predetermined angle, wherein theplurality of first rectangular regions and the plurality of secondrectangular regions correspond to each other, respectively, wherein thediagnosis assistance model labels whether a plaque included in thesecond rectangular regions corresponding to the first rectangularregions contains a lipid component, wherein the diagnosis assistancemodel labels a location of the lipid component based on locations of thefirst rectangular regions and the second rectangular regions, whereinthe diagnosis assistance model is labeled with a fifth color when theplaque included in the second rectangular regions contains a lipidcomponent, wherein the diagnosis assistance model is labeled with asixth color when the plaque included in the second rectangular regionsdoes not contain a lipid component, and wherein the fifth color and thesixth color are different.
 8. The method of claim 1, wherein the firstblood vessel image is an ultrasound image of a cardiovascular, andwherein the second blood vessel image includes a result of near-infraredspectral spectrum of cardiovascular, a result of virtual histologicalanalysis, or the first blood vessel image on which information of theobject is labeled.
 9. The method of claim 1, wherein the diagnosisassistance model includes a classification model for classifying thesecond blood vessel image according to an image capturing device orlabeling of the second blood vessel image, and wherein informationlabeled on the first blood vessel image corresponding to the secondblood vessel image is different according to a classification result ofthe classification model.
 10. A non-transitory computer-readable storagemedium storing instructions, the instructions when executed by aprocessor cause the processor to: obtain a first image of at least apartial region of a cardiovascular of a subject; and obtain a secondimage by processing the first image based on a diagnosis assistancemodel, the diagnosis assistance model being trained based on a firstblood vessel image and a second blood vessel image corresponding to thefirst blood vessel image as a labeled data; wherein the first image andthe first blood vessel image are taken with a same imaging method,wherein the second image is generated based on the first image toinclude analysis information on at least a partial region of the firstimage, wherein an object of the second blood vessel image corresponds toan object of the first blood vessel image, wherein a location of thecardiovascular vessel captured in the second blood vessel imagecorresponds to a location of the cardiovascular vessel captured in thefirst blood vessel image, and wherein the first blood vessel image andthe second blood vessel image are taken with different imaging methods.11. An electronic device for analyzing a cardiovascular image of asubject comprising at least one processor configured to: obtain a firstimage of at least a partial region of a cardiovascular of the subject,and obtain a second image by processing the first image based on adiagnosis assistance model, the diagnosis assistance model being trainedbased on a first blood vessel image and a second blood vessel imagecorresponding to the first blood vessel image as a labeled data, whereinthe first image and the first blood vessel image are taken with a sameimaging method, wherein the second image is generated based on the firstimage to include analysis information on at least a partial region ofthe first image, wherein an object of the second blood vessel imagecorresponds to an object of the first blood vessel image, wherein alocation of the cardiovascular vessel captured in the second bloodvessel image corresponds to a location of the cardiovascular vesselcaptured in the first blood vessel image, and wherein the first bloodvessel image and the second blood vessel image are taken with differentimaging methods.
 12. The electronic device of claim 11, wherein theanalysis information includes numerical information or color informationbased on a type of component of a plaque included in at least a partialregion of the first image.
 13. The electronic device of claim 11,wherein the analysis information includes color information displayed sothat the location of a lipid component and the blood vessel outer wallregion included in the first image correspond, in case that the analysisinformation includes information on whether a plaque contains a lipidcomponent and the location of the lipid component.
 14. The electronicdevice of claim 11, wherein the analysis information is displayed in afirst color indicating that a component of a plaque contains a lipidcomponent or a second color indicating that the component of the plaquedoes not contain a lipid component, and wherein the first color and thesecond color are different.
 15. The electronic device of claim 11,wherein the diagnosis assistance model labels whether a plaque includedin the second blood vessel image contains a lipid component, wherein thediagnosis assistance model is labeled with a third color in case thatthe plaque included in the second blood vessel image contains a lipidcomponent, and wherein the diagnosis assistance model is labeled with afourth color in case that the plaque included in the second blood vesselimage does not contain the lipid component, and the third color and thefourth color are different.
 16. The electronic device of claim 11,wherein the diagnosis assistance model labels whether a plaque includedin the second blood vessel image contains a lipid component and alocation of the plaque.
 17. The electronic device of claim 11, whereinthe first blood vessel image is divided into a plurality of firstrectangular regions rotated at a predetermined angle of 10 degrees orless, wherein the second blood vessel image is divided into a pluralityof second rectangular regions rotated at the predetermined angle,wherein the plurality of first rectangular regions and the plurality ofsecond rectangular regions correspond to each other, respectively,wherein the diagnosis assistance model labels whether a plaque includedin the second rectangular regions corresponding to the first rectangularregions contains a lipid component, wherein the diagnosis assistancemodel labels a location of the lipid component based on locations of thefirst rectangular regions and the second rectangular regions, whereinthe diagnosis assistance model is labeled with a fifth color when theplaque included in the second rectangular regions contains a lipidcomponent, wherein the diagnosis assistance model is labeled with asixth color when the plaque included in the second rectangular regionsdoes not contain a lipid component, and wherein the fifth color and thesixth color are different.
 18. The electronic device of claim 11,wherein the first blood vessel image is an ultrasound image of acardiovascular, and wherein the second blood vessel image includes aresult of near-infrared spectral spectrum of cardiovascular, a result ofvirtual histological analysis, and the first blood vessel image on whichinformation of an object is labeled.
 19. The electronic device of claim11, wherein the diagnosis assistance model includes a classificationmodel for classifying the second blood vessel image according to animage capturing device or labeling of the second blood vessel image, andwherein information labeled on the first blood vessel imagecorresponding to the second blood vessel image is different according toa classification result of the classification model.